This paper presents FIR digital filters based on stochastic/binary hybrid computation with reduced hardware complexity and high computational accuracy. Recently, some attempts have been made to apply stochastic computation to realization of digital filters. Such realization methods lead to significant reduction of hardware complexity over the conventional filter realizations based on binary computation. However, the stochastic digital filters suffer from lower computational accuracy than the digital filters based on binary computation because of the random error fluctuations that are generated in stochastic bit streams, stochastic multipliers, and stochastic adders. This becomes a serious problem in the case of FIR filter realizations compared with the IIR counterparts because FIR filters usually require larger number of multiplications and additions than IIR filters. To improve the computational accuracy, this paper presents a stochastic/binary hybrid realization, where multipliers are realized using stochastic computation but adders are realized using binary computation. In addition, a coefficient-scaling technique is proposed to further improve the computational accuracy of stochastic FIR filters. Furthermore, the transposed structure is applied to the FIR filter realization, leading to reduction of hardware complexity. Evaluation results demonstrate that our method achieves at most 40dB improvement in minimum stopband attenuation compared with the conventional pure stochastic design.

Web tracking is widely used as a means to track user's behavior on websites. While web tracking provides new opportunities of e-commerce, it also includes certain risks such as privacy infringement. Therefore, analyzing such risks in the wild Internet is meaningful to make the user's privacy transparent. This work aims to understand how the web tracking has been adopted to prominent websites. We also aim to understand their resilience to the ad-blocking techniques. Web tracking-enabled websites collect the information called the web browser fingerprints, which can be used to identify users. We develop a scalable system that can detect fingerprinting by using both dynamic and static analyses. If a tracking site makes use of many and strong fingerprints, the site is likely resilient to the ad-blocking techniques. We also analyze the connectivity of the third-party tracking sites, which are linked from multiple websites. The link analysis allows us to extract the group of associated tracking sites and understand how influential these sites are. Based on the analyses of 100,000 websites, we quantify the potential risks of the web tracking-enabled websites. We reveal that there are 226 websites that adopt fingerprints that cannot be detected with the most of off-the-shelf anti-tracking tools. We also reveal that a major, resilient third-party tracking site is linked to 50.0 % of the top-100,000 popular websites.

We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., “walking,” “in moving vehicle,” or “other.” Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.

An enormous number of malware samples pose a major threat to our networked society. Antivirus software and intrusion detection systems are widely implemented on the hosts and networks as fundamental countermeasures. However, they may fail to detect evasive malware. Thus, setting a high priority for new varieties of malware is necessary to conduct in-depth analyses and take preventive measures. In this paper, we present a traffic model for malware that can classify network behaviors of malware and identify new varieties of malware. Our model comprises malware-specific features and general traffic features that are extracted from packet traces obtained from a dynamic analysis of the malware. We apply a clustering analysis to generate a classifier and evaluate our proposed model using large-scale live malware samples. The results of our experiment demonstrate the effectiveness of our model in finding new varieties of malware.

Distributed Denial of Service (DDoS) attacks based on HTTP and HTTPS (i.e., HTTP(S)-DDoS) are increasingly popular among attackers. Overlay-based mitigation solutions attract small and medium-sized enterprises mainly for their low cost and high scalability. However, conventional overlay-based solutions assume content inspection to remotely mitigate HTTP(S)-DDoS attacks, prompting trust concerns. This paper reports on a new overlay-based method which practically adds a third level of client identification (to conventional per-IP and per-connection). This enhanced identification enables remote mitigation of more complex HTTP(S)-DDoS categories without content inspection. A novel behavior-based reputation and penalty system is designed, then a simplified proof of concept prototype is implemented and deployed on DeterLab. Among several conducted experiments, two are presented in this paper representing a single-vector and a multi-vector complex HTTP(S)-DDoS attack scenarios (utilizing LOIC, Slowloris, and a custom-built attack tool for HTTPS-DDoS). Results show nearly 99.2% reduction in attack traffic and 100% chance of legitimate service. Yet, attack reduction decreases, and cost in service time (of a specified file) rises, temporarily during an approximately 2 minutes mitigation time. Collateral damage to non-attacking clients sharing an attack IP is measured in terms of a temporary extra service time. Only the added identification level was utilized for mitigation, while future work includes incorporating all three levels to mitigate switching and multi-request per connection attack categories.

This letter presents a successive partial interference cancellation (SPIC) scheme for full-duplex (FD) and multiple-input multiple-output (MIMO) relaying system. The proposed scheme coordinates the cancellation for the self-interference and inter-stream interference. The objective for the coordination focuses on simultaneously minimizing the two interferences. Simulation results under the measured data show that the system with the proposed scheme can achieve a significant performance gain compared to the conventional FD and half-duplex (HD) systems.

This letter studies the price-based power control algorithm for the spectrum sharing cognitive radio networks. The primary user (PU) profits from the secondary users (SUs) by pricing the interference power made by them. The SUs cooperate with each other to maximize their sum revenue with the signal-to-interference plus noise ratio (SINR) balancing condition. The interaction between the PU and the SUs is modeled as a Stackelberg game. Closed-form expressions of the optimal price for the PU and power allocation for the SUs are given. Simulation results show the proposed algorithm improves the revenue of both the PU and fairness of the SUs compared with the uniform pricing algorithm.

The widespread usage of computers and communication networks affects people's social activities effectively in terms of intercommunication and the communication generally begins with domain name resolutions which are mainly provided by DNS (Domain Name System). Meanwhile, continuous cyber threats to DNS such as cache poisoning also affects computer networks critically. DNSSEC (DNS Security Extensions) is designed to provide secure name resolution between authoritative zone servers and DNS full resolvers. However high workload of DNSSEC validation on DNS full resolvers and complex key management on authoritative zone servers hinder its wide deployment. Moreover, querying clients use the name resolution results validated on DNS full resolvers, therefore they only get errors when DNSSEC validation fails or times out. In addition, name resolution failure can occur on querying clients due to technical and operational issues of DNSSEC. In this paper, we propose a client based DNSSEC validation system with adaptive alert mechanism considering minimal querying client timeout. The proposed system notifies the user of alert messages with answers even when the DNSSEC validation on the client fails or timeout so that the user can determine how to handle the received answers. We also implemented a prototype system and evaluated the features on a local experimental network as well as in the Internet. The contribution of this article is that the proposed system not only can mitigate the workload of DNS full resolvers but also can cover querying clients with secure name resolution, and by solving the existing operation issues in DNSSEC, it also can promote DNSSEC deployment.

In this paper, we propose a novel method for the design of CSD (Canonic Signed Digit) coefficient FIR (Finite Impulse Response) filters based on ACO (Ant Colony Optimization). This design problem is formulated as a combinatorial optimization problem and requires high computation time to obtain the optimal solution. Therefore, we propose an ACO approach for the design of CSD coefficient FIR filters. ACO is one of the promising approaches and appropriate for solving a combinatorial optimization problem in reasonable computation time. Several design examples showed the effectiveness of our method.

To overcome the false lock or detection missing problems caused by the multiple peaks of the auto-correlation function (ACF) of Binary Offset Carrier (BOC) modulated signal, an acquisition algorithm based on unit correlation for BOC(n,n) signal is proposed in this paper. The local BOC signal is separated into two unit signals, an odd one and an even one. Then a reconstruction of the unit correlation functions between the unit signals and the received BOC signal is performed and M sections of reconstructed correlation function are accumulated according to the non-coherent method, so that this novel acquisition algorithm can not only eliminate the multiple secondary peaks, but also retain the advantage of the narrow correlation main peak. Simulation results show that the acquisition sensitivity of the proposed algorithm is increased 3dBHz compared with the ASPeCT method, and the computation cost is only 41.46% of the ASPeCT method when M=2.

Discrete structure manipulation is a fundamental technique for many problems solved by computers. BDDs/ZDDs have attracted a great deal of attention for twenty years, because those data structures are useful to efficiently manipulate basic discrete structures such as logic functions and sets of combinations. Recently, one of the most interesting research topics related to BDDs/ZDDs is Frontier-based search method, a very efficient algorithm for enumerating and indexing the subsets of a graph to satisfy a given constraint. This work is important because many kinds of practical problems can be efficiently solved by some variations of this algorithm. In this article, we present recent research activity related to BDD and ZDD. We first briefly explain the basic techniques for BDD/ZDD manipulation, and then we present several examples of the state-of-the-art algorithms to show the power of enumeration.

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (γ-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, γ-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.

Process and temperature variations have become a serious concern for ultra-low voltage (ULV) technology. The clock generator is the essential component for the ULV very-large-scale integration (VLSI). MOSFETs that are operated in the sub-threshold region are widely applied for ULV technology. However, MOSFETs at subthreshold region have relatively high variations with process and temperature. In this paper, process and temperature variations on the clock generators have been studied. This paper presents an ultra-low voltage 2.4GHz CMOS voltage controlled oscillator with temperature and process compensation. A new all-digital auto compensated mechanism to reduce process and temperature variation without any laser trimming is proposed. With the compensated circuit, the VCO frequency-drift is 16.6 times the improvements of the uncompensated one as temperature changes. Furthermore, it also provides low jitter performance.

For network researchers and practitioners, active measurement, in which probe packets are injected into a network, is a powerful tool to measure end-to-end delay. It is, however, suffers the intrusiveness problem, where the load of the probe traffic itself affects the network QoS. In this paper, we first demonstrate that there exists a fundamental accuracy bound of the conventional active measurement of delay. Second, to transcend that bound, we propose INTrusiveness-aware ESTimation (INTEST), an approach that compensates for the delays produced by probe packets in wired networks. Simulations of M/M/1 and MMPP/M/1 show that INTEST enables a more accurate estimation of end-to-end delay than conventional methods. Furthermore, we extend INTEST for multi-hop networks by using timestamps or multi-flow probes.

In this paper, we clarify the issues in a metropolitan environment involving overlying frequency bands with various bandwidths and propose a cell selection scheme that improves the communications quality based on user and network characteristics. Different frequency bands with various signal bandwidths will be overlaid on each other in forthcoming fifth-generation (5G) radio access networks. At the same time, services, applications or features of sets of user equipment (UEs) will become more diversified and the requirements for the quality of communications will become more varied. Moreover, in real environments, roads and buildings have irregular constructions. Especially in an urban or metropolitan environment, the complex architecture present in a metropolis directly affects radio propagation. Under these conditions, the communications quality is degraded because cell radio resources are depleted due to many UE connections and the mismatch between service requirements and cell capabilities. The proposed scheme prevents this degradation in communications quality. The effectiveness of the proposed scheme is evaluated in an ideal regular deployment and in a non-regular metropolitan environment based on computer simulations. Simulation results show that the average of the time for the proposed scheme from the start of transmission to the completion of reception at the UE is improved by approximately 40% compared to an existing cell selection scheme that is based on the Maximum Signal-to-Interference plus Noise power Ratio (SINR).

Midori128 is a lightweight block cipher proposed at ASIACRYPT 2015 to achieve low energy consumption per bit. Currently, the best published impossible differential attack on Midori128 covers 10 rounds without the pre-whitening key. By exploiting the special structure of the S-boxes and the binary linear transformation layer in Midori128, we present impossible differential distinguishers that cover 7 full rounds including the mix column operations. Then, we exploit four of these distinguishers to launch multiple impossible differential attack against 11 rounds of the cipher with the pre-whitening and post-whitening keys.

Massive multiple-input multiple-output (MIMO) technology is one of the key enablers in the fifth generation mobile communications (5G), in order to accommodate growing traffic demands and to utilize higher super high frequency (SHF) and extremely high frequency (EHF) bands. In the paper, we propose a novel transmit precoding named “nonlinear block multi-diagonalization (NL-BMD) precoding” for multiuser MIMO (MU-MIMO) downlink toward 5G. Our NL-BMD precoding strategy is composed of two essential techniques: block multi-diagonalization (BMD) and adjacent inter-user interference pre-cancellation (IUI-PC). First, as an extension of the conventional block diagonalization (BD) method, the linear BMD precoder for the desired user is computed to incorporate a predetermined number of interfering users, in order to ensure extra degrees of freedom at the transmit array even after null steering. Additionally, adjacent IUI-PC, as a nonlinear operation, is introduced to manage the residual interference partially allowed in BMD computation, with effectively-reduced numerical complexity. It is revealed through computer simulations that the proposed NL-BMD precoding yields up to 67% performance improvement in average sum-rate spectral efficiency and enables large-capacity transmission regardless of the user distribution, compared with the conventional BD precoding.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

Many-core architecture is becoming an attractive design choice in high-end embedded systems design. There are, however, many important design issues, and load balancing is one of them. In this work, we take the approach of diffusive load balancing which enables autonomic load distribution in many-core systems. We improve the existing schemes by adding the concept of simulated annealing for more effective load distribution. The modified scheme is also capable of managing a situation of non-uniform granularity of task loading, which the existing ones cannot. In addition, the suggested scheme is extended to be able to handle dependencies existing in task graphs where tasks have communications between each other. As experiments, we tried various existing schemes as well as the proposed one to map synthetic applications and real world applications on a many-core architecture with 21 cores and 4 memory tiles. For the applications without communications, the experiments show that the proposed scheme gives the best results in terms of peak load and standard deviation. For real applications such as mp3 decoder and h.263 encoder which have communications between tasks, we show the effectiveness of our communication-aware scheme for load balancing in terms of throughput.

The increasing demand on scalability and reusability of system-on-chip design as well as the decoupling between computation and communication has motivated the growth of the Network-on-Chip (NoC) paradigm in the last decade. In NoC-based systems, the computational resources (i.e. IPs) communicate with each other using a network infrastructure. Many works have focused on the development of NoC architectures and routing mechanisms, while the interfacing between network and associated IPs also needs to be considered. In this paper, we present a novel efficient AXI (AMBA eXtensible Interface) compliant network adapter for NoC architectures, which is named an AXI-NoC adapter. The proposed network adapter achieves high communication throughput of 20.8Gbits/s and consumes 4.14mW at the operating frequency of 650MHz. It has a low area footprint (952 gates, approximate to 2,793µm2 with CMOS 45nm technology) thanks to its effective hybrid micro-architectures and with zero latency thanks to the proposed mux-selection method.